{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chapter 13 - Data Science\n",
    "## Data Manipulation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 0 - Setting up the notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "import arrow\n",
    "import pandas as pd\n",
    "from pandas import DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1- Loading Data into a DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cmp_name</th>\n",
       "      <th>cmp_bgt</th>\n",
       "      <th>cmp_spent</th>\n",
       "      <th>cmp_clicks</th>\n",
       "      <th>cmp_impr</th>\n",
       "      <th>user</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KTR_20250404_20250916_35-50_A_EUR</td>\n",
       "      <td>964496</td>\n",
       "      <td>29586</td>\n",
       "      <td>36632</td>\n",
       "      <td>500001</td>\n",
       "      <td>{\"username\": \"epennington\", \"name\": \"Stephanie...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AKX_20240130_20241017_20-25_M_GBP</td>\n",
       "      <td>344739</td>\n",
       "      <td>166010</td>\n",
       "      <td>67325</td>\n",
       "      <td>499999</td>\n",
       "      <td>{\"username\": \"epennington\", \"name\": \"Stephanie...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BYU_20230828_20250115_25-45_M_GBP</td>\n",
       "      <td>177403</td>\n",
       "      <td>125738</td>\n",
       "      <td>29989</td>\n",
       "      <td>499997</td>\n",
       "      <td>{\"username\": \"joshua61\", \"name\": \"Diana Richar...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AKX_20250216_20261129_45-60_F_USD</td>\n",
       "      <td>618256</td>\n",
       "      <td>75017</td>\n",
       "      <td>76301</td>\n",
       "      <td>500000</td>\n",
       "      <td>{\"username\": \"joshua61\", \"name\": \"Diana Richar...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AKX_20231229_20250721_20-40_F_GBP</td>\n",
       "      <td>113805</td>\n",
       "      <td>12583</td>\n",
       "      <td>48915</td>\n",
       "      <td>500001</td>\n",
       "      <td>{\"username\": \"joshua61\", \"name\": \"Diana Richar...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            cmp_name  cmp_bgt  cmp_spent  cmp_clicks  \\\n",
       "0  KTR_20250404_20250916_35-50_A_EUR   964496      29586       36632   \n",
       "1  AKX_20240130_20241017_20-25_M_GBP   344739     166010       67325   \n",
       "2  BYU_20230828_20250115_25-45_M_GBP   177403     125738       29989   \n",
       "3  AKX_20250216_20261129_45-60_F_USD   618256      75017       76301   \n",
       "4  AKX_20231229_20250721_20-40_F_GBP   113805      12583       48915   \n",
       "\n",
       "   cmp_impr                                               user  \n",
       "0    500001  {\"username\": \"epennington\", \"name\": \"Stephanie...  \n",
       "1    499999  {\"username\": \"epennington\", \"name\": \"Stephanie...  \n",
       "2    499997  {\"username\": \"joshua61\", \"name\": \"Diana Richar...  \n",
       "3    500000  {\"username\": \"joshua61\", \"name\": \"Diana Richar...  \n",
       "4    500001  {\"username\": \"joshua61\", \"name\": \"Diana Richar...  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load data from a json file into a DataFrame\n",
    "df = pd.read_json(\"data.json\")\n",
    "\n",
    "# let's take a peek at the first 5 rows, to make sure\n",
    "# nothing weird has happened\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cmp_name      5065\n",
       "cmp_bgt       5065\n",
       "cmp_spent     5065\n",
       "cmp_clicks    5065\n",
       "cmp_impr      5065\n",
       "user          5065\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let us see how many values there are  in each column\n",
    "df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cmp_bgt</th>\n",
       "      <th>cmp_spent</th>\n",
       "      <th>cmp_clicks</th>\n",
       "      <th>cmp_impr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5065.000000</td>\n",
       "      <td>5065.000000</td>\n",
       "      <td>5065.000000</td>\n",
       "      <td>5065.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>502965.054097</td>\n",
       "      <td>253389.854689</td>\n",
       "      <td>40265.781639</td>\n",
       "      <td>499999.474630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>290468.998656</td>\n",
       "      <td>222774.897138</td>\n",
       "      <td>21840.783154</td>\n",
       "      <td>2.023801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1764.000000</td>\n",
       "      <td>107.000000</td>\n",
       "      <td>899.000000</td>\n",
       "      <td>499992.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>251171.000000</td>\n",
       "      <td>67071.000000</td>\n",
       "      <td>22575.000000</td>\n",
       "      <td>499998.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>500694.000000</td>\n",
       "      <td>187743.000000</td>\n",
       "      <td>36746.000000</td>\n",
       "      <td>499999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>756850.000000</td>\n",
       "      <td>391790.000000</td>\n",
       "      <td>55817.000000</td>\n",
       "      <td>500001.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>999565.000000</td>\n",
       "      <td>984705.000000</td>\n",
       "      <td>98379.000000</td>\n",
       "      <td>500007.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             cmp_bgt      cmp_spent    cmp_clicks       cmp_impr\n",
       "count    5065.000000    5065.000000   5065.000000    5065.000000\n",
       "mean   502965.054097  253389.854689  40265.781639  499999.474630\n",
       "std    290468.998656  222774.897138  21840.783154       2.023801\n",
       "min      1764.000000     107.000000    899.000000  499992.000000\n",
       "25%    251171.000000   67071.000000  22575.000000  499998.000000\n",
       "50%    500694.000000  187743.000000  36746.000000  499999.000000\n",
       "75%    756850.000000  391790.000000  55817.000000  500001.000000\n",
       "max    999565.000000  984705.000000  98379.000000  500007.000000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cmp_name</th>\n",
       "      <th>cmp_bgt</th>\n",
       "      <th>cmp_spent</th>\n",
       "      <th>cmp_clicks</th>\n",
       "      <th>cmp_impr</th>\n",
       "      <th>user</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3186</th>\n",
       "      <td>GRZ_20230914_20230929_40-60_A_EUR</td>\n",
       "      <td>999565</td>\n",
       "      <td>358923</td>\n",
       "      <td>63869</td>\n",
       "      <td>499998</td>\n",
       "      <td>{\"username\": \"zsmith\", \"name\": \"Kimberly Stoke...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3168</th>\n",
       "      <td>KTR_20250315_20260507_25-40_M_USD</td>\n",
       "      <td>999487</td>\n",
       "      <td>707699</td>\n",
       "      <td>21097</td>\n",
       "      <td>500000</td>\n",
       "      <td>{\"username\": \"brenda48\", \"name\": \"Brian Holden...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3624</th>\n",
       "      <td>GRZ_20250227_20250617_30-45_F_USD</td>\n",
       "      <td>999482</td>\n",
       "      <td>153038</td>\n",
       "      <td>3435</td>\n",
       "      <td>499998</td>\n",
       "      <td>{\"username\": \"seanlee\", \"name\": \"Kenneth Gonza...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               cmp_name  cmp_bgt  cmp_spent  cmp_clicks  \\\n",
       "3186  GRZ_20230914_20230929_40-60_A_EUR   999565     358923       63869   \n",
       "3168  KTR_20250315_20260507_25-40_M_USD   999487     707699       21097   \n",
       "3624  GRZ_20250227_20250617_30-45_F_USD   999482     153038        3435   \n",
       "\n",
       "      cmp_impr                                               user  \n",
       "3186    499998  {\"username\": \"zsmith\", \"name\": \"Kimberly Stoke...  \n",
       "3168    500000  {\"username\": \"brenda48\", \"name\": \"Brian Holden...  \n",
       "3624    499998  {\"username\": \"seanlee\", \"name\": \"Kenneth Gonza...  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let us see which are the top 3 campaigns according\n",
    "# to budget (regardless of the currency)\n",
    "df.sort_values(by=[\"cmp_bgt\"], ascending=False).head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cmp_name</th>\n",
       "      <th>cmp_bgt</th>\n",
       "      <th>cmp_spent</th>\n",
       "      <th>cmp_clicks</th>\n",
       "      <th>cmp_impr</th>\n",
       "      <th>user</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>725</th>\n",
       "      <td>GRZ_20240202_20250520_45-60_F_USD</td>\n",
       "      <td>1991</td>\n",
       "      <td>1070</td>\n",
       "      <td>65879</td>\n",
       "      <td>499999</td>\n",
       "      <td>{\"username\": \"burnsmark\", \"name\": \"Kayla Vega\"...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1032</th>\n",
       "      <td>GRZ_20231018_20241220_35-55_A_GBP</td>\n",
       "      <td>1839</td>\n",
       "      <td>1589</td>\n",
       "      <td>54180</td>\n",
       "      <td>499998</td>\n",
       "      <td>{\"username\": \"kimberly05\", \"name\": \"Robin Brow...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4327</th>\n",
       "      <td>KTR_20240411_20260318_45-60_F_EUR</td>\n",
       "      <td>1764</td>\n",
       "      <td>1192</td>\n",
       "      <td>19340</td>\n",
       "      <td>499994</td>\n",
       "      <td>{\"username\": \"catherineherrera\", \"name\": \"Bren...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               cmp_name  cmp_bgt  cmp_spent  cmp_clicks  \\\n",
       "725   GRZ_20240202_20250520_45-60_F_USD     1991       1070       65879   \n",
       "1032  GRZ_20231018_20241220_35-55_A_GBP     1839       1589       54180   \n",
       "4327  KTR_20240411_20260318_45-60_F_EUR     1764       1192       19340   \n",
       "\n",
       "      cmp_impr                                               user  \n",
       "725     499999  {\"username\": \"burnsmark\", \"name\": \"Kayla Vega\"...  \n",
       "1032    499998  {\"username\": \"kimberly05\", \"name\": \"Robin Brow...  \n",
       "4327    499994  {\"username\": \"catherineherrera\", \"name\": \"Bren...  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can also use 'tail' to get the bottom 3 campaigns\n",
    "df.sort_values(by=[\"cmp_bgt\"], ascending=False).tail(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2 - Manipulating the DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Unpack campaign names\n",
    "First, we will explode `cmp_name` into its components and get a separate DataFrame for those."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def unpack_campaign_name(name):\n",
    "    # very optimistic method, assumes data in campaign name\n",
    "    # is always in good state\n",
    "    type_, start, end, age, gender, currency = name.split(\"_\")\n",
    "    start = arrow.get(start, \"YYYYMMDD\").date()\n",
    "    end = arrow.get(end, \"YYYYMMDD\").date()\n",
    "    return type_, start, end, age, gender, currency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply unpack_campaign_name() to every row in the cmp_name column.\n",
    "campaign_data = df[\"cmp_name\"].apply(unpack_campaign_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Type</th>\n",
       "      <th>Start</th>\n",
       "      <th>End</th>\n",
       "      <th>Target Age</th>\n",
       "      <th>Target Gender</th>\n",
       "      <th>Currency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KTR</td>\n",
       "      <td>2025-04-04</td>\n",
       "      <td>2025-09-16</td>\n",
       "      <td>35-50</td>\n",
       "      <td>A</td>\n",
       "      <td>EUR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AKX</td>\n",
       "      <td>2024-01-30</td>\n",
       "      <td>2024-10-17</td>\n",
       "      <td>20-25</td>\n",
       "      <td>M</td>\n",
       "      <td>GBP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BYU</td>\n",
       "      <td>2023-08-28</td>\n",
       "      <td>2025-01-15</td>\n",
       "      <td>25-45</td>\n",
       "      <td>M</td>\n",
       "      <td>GBP</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Type       Start         End Target Age Target Gender Currency\n",
       "0  KTR  2025-04-04  2025-09-16      35-50             A      EUR\n",
       "1  AKX  2024-01-30  2024-10-17      20-25             M      GBP\n",
       "2  BYU  2023-08-28  2025-01-15      25-45             M      GBP"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "campaign_cols = [\n",
    "    \"Type\",\n",
    "    \"Start\",\n",
    "    \"End\",\n",
    "    \"Target Age\",\n",
    "    \"Target Gender\",\n",
    "    \"Currency\",\n",
    "]\n",
    "campaign_df = DataFrame.from_records(\n",
    "    campaign_data, columns=campaign_cols, index=df.index\n",
    ")\n",
    "campaign_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# let us join the two dataframes\n",
    "df = df.join(campaign_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cmp_name</th>\n",
       "      <th>Type</th>\n",
       "      <th>Start</th>\n",
       "      <th>End</th>\n",
       "      <th>Target Age</th>\n",
       "      <th>Target Gender</th>\n",
       "      <th>Currency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KTR_20250404_20250916_35-50_A_EUR</td>\n",
       "      <td>KTR</td>\n",
       "      <td>2025-04-04</td>\n",
       "      <td>2025-09-16</td>\n",
       "      <td>35-50</td>\n",
       "      <td>A</td>\n",
       "      <td>EUR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AKX_20240130_20241017_20-25_M_GBP</td>\n",
       "      <td>AKX</td>\n",
       "      <td>2024-01-30</td>\n",
       "      <td>2024-10-17</td>\n",
       "      <td>20-25</td>\n",
       "      <td>M</td>\n",
       "      <td>GBP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BYU_20230828_20250115_25-45_M_GBP</td>\n",
       "      <td>BYU</td>\n",
       "      <td>2023-08-28</td>\n",
       "      <td>2025-01-15</td>\n",
       "      <td>25-45</td>\n",
       "      <td>M</td>\n",
       "      <td>GBP</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            cmp_name Type       Start         End Target Age  \\\n",
       "0  KTR_20250404_20250916_35-50_A_EUR  KTR  2025-04-04  2025-09-16      35-50   \n",
       "1  AKX_20240130_20241017_20-25_M_GBP  AKX  2024-01-30  2024-10-17      20-25   \n",
       "2  BYU_20230828_20250115_25-45_M_GBP  BYU  2023-08-28  2025-01-15      25-45   \n",
       "\n",
       "  Target Gender Currency  \n",
       "0             A      EUR  \n",
       "1             M      GBP  \n",
       "2             M      GBP  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# inspect the data to make sure everything matches\n",
    "df[[\"cmp_name\"] + campaign_cols].head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Unpack user data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def unpack_user_json(user):\n",
    "    # very optimistic as well, expects user objects\n",
    "    # to have all attributes\n",
    "    user = json.loads(user.strip())\n",
    "    return [\n",
    "        user[\"username\"],\n",
    "        user[\"email\"],\n",
    "        user[\"name\"],\n",
    "        user[\"gender\"],\n",
    "        user[\"age\"],\n",
    "        user[\"address\"],\n",
    "    ]\n",
    "\n",
    "\n",
    "user_data = df[\"user\"].apply(unpack_user_json)\n",
    "user_cols = [\n",
    "    \"Username\",\n",
    "    \"Email\",\n",
    "    \"Name\",\n",
    "    \"Gender\",\n",
    "    \"Age\",\n",
    "    \"Address\",\n",
    "]\n",
    "user_df = DataFrame(\n",
    "    user_data.tolist(), columns=user_cols, index=df.index\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user</th>\n",
       "      <th>Username</th>\n",
       "      <th>Email</th>\n",
       "      <th>Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Address</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>{\"username\": \"epennington\", \"name\": \"Stephanie...</td>\n",
       "      <td>epennington</td>\n",
       "      <td>harmontammy@example.com</td>\n",
       "      <td>Stephanie Gonzalez</td>\n",
       "      <td>F</td>\n",
       "      <td>44</td>\n",
       "      <td>48185 Brittany Green Suite 999\\nEast Tanya, KS...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>{\"username\": \"epennington\", \"name\": \"Stephanie...</td>\n",
       "      <td>epennington</td>\n",
       "      <td>harmontammy@example.com</td>\n",
       "      <td>Stephanie Gonzalez</td>\n",
       "      <td>F</td>\n",
       "      <td>44</td>\n",
       "      <td>48185 Brittany Green Suite 999\\nEast Tanya, KS...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                user     Username  \\\n",
       "0  {\"username\": \"epennington\", \"name\": \"Stephanie...  epennington   \n",
       "1  {\"username\": \"epennington\", \"name\": \"Stephanie...  epennington   \n",
       "\n",
       "                     Email                Name Gender  Age  \\\n",
       "0  harmontammy@example.com  Stephanie Gonzalez      F   44   \n",
       "1  harmontammy@example.com  Stephanie Gonzalez      F   44   \n",
       "\n",
       "                                             Address  \n",
       "0  48185 Brittany Green Suite 999\\nEast Tanya, KS...  \n",
       "1  48185 Brittany Green Suite 999\\nEast Tanya, KS...  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's join the two dataframes\n",
    "df = df.join(user_df)\n",
    "\n",
    "# inspect again to make sure everything matches\n",
    "df[[\"user\"] + user_cols].head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rename columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# now we have the DataFrame completely expanded, let us fix those ugly column names\n",
    "new_column_names = {\n",
    "    \"cmp_bgt\": \"Budget\",\n",
    "    \"cmp_spent\": \"Spent\",\n",
    "    \"cmp_clicks\": \"Clicks\",\n",
    "    \"cmp_impr\": \"Impressions\",\n",
    "}\n",
    "df.rename(columns=new_column_names, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3 - Compute Some metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_metrics(df):\n",
    "    # Click Through Rate\n",
    "    df[\"CTR\"] = df[\"Clicks\"] / df[\"Impressions\"]\n",
    "    # Cost Per Click\n",
    "    df[\"CPC\"] = df[\"Spent\"] / df[\"Clicks\"]\n",
    "    # Cost Per Impression\n",
    "    df[\"CPI\"] = df[\"Spent\"] / df[\"Impressions\"]\n",
    "\n",
    "\n",
    "calculate_metrics(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Spent</th>\n",
       "      <th>Clicks</th>\n",
       "      <th>Impressions</th>\n",
       "      <th>CTR</th>\n",
       "      <th>CPC</th>\n",
       "      <th>CPI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>29586</td>\n",
       "      <td>36632</td>\n",
       "      <td>500001</td>\n",
       "      <td>0.073264</td>\n",
       "      <td>0.807655</td>\n",
       "      <td>0.059172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>166010</td>\n",
       "      <td>67325</td>\n",
       "      <td>499999</td>\n",
       "      <td>0.134650</td>\n",
       "      <td>2.465800</td>\n",
       "      <td>0.332021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>125738</td>\n",
       "      <td>29989</td>\n",
       "      <td>499997</td>\n",
       "      <td>0.059978</td>\n",
       "      <td>4.192804</td>\n",
       "      <td>0.251478</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Spent  Clicks  Impressions       CTR       CPC       CPI\n",
       "0   29586   36632       500001  0.073264  0.807655  0.059172\n",
       "1  166010   67325       499999  0.134650  2.465800  0.332021\n",
       "2  125738   29989       499997  0.059978  4.192804  0.251478"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"Spent\", \"Clicks\", \"Impressions\", \"CTR\", \"CPC\", \"CPI\"]].head(\n",
    "    3\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CTR: 0.07326385347229306 0.07326385347229306\n",
      "CPC: 0.8076545097182791 0.8076545097182791\n",
      "CPI: 0.059171881656236686 0.059171881656236686\n"
     ]
    }
   ],
   "source": [
    "# let us take the values of the first row and verify\n",
    "clicks = df[\"Clicks\"][0]\n",
    "impressions = df[\"Impressions\"][0]\n",
    "spent = df[\"Spent\"][0]\n",
    "\n",
    "CTR = df[\"CTR\"][0]\n",
    "CPC = df[\"CPC\"][0]\n",
    "CPI = df[\"CPI\"][0]\n",
    "\n",
    "print(\"CTR:\", CTR, clicks / impressions)\n",
    "print(\"CPC:\", CPC, spent / clicks)\n",
    "print(\"CPI:\", CPI, spent / impressions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also want the day of the week when each campaign started, and its duration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_day_of_the_week(day):\n",
    "    return day.strftime(\"%A\")\n",
    "\n",
    "\n",
    "def get_duration(row):\n",
    "    return (row[\"End\"] - row[\"Start\"]).days\n",
    "\n",
    "\n",
    "df[\"Day of Week\"] = df[\"Start\"].apply(get_day_of_the_week)\n",
    "df[\"Duration\"] = df.apply(get_duration, axis=\"columns\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Start</th>\n",
       "      <th>End</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Day of Week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2025-04-04</td>\n",
       "      <td>2025-09-16</td>\n",
       "      <td>165</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2024-01-30</td>\n",
       "      <td>2024-10-17</td>\n",
       "      <td>261</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023-08-28</td>\n",
       "      <td>2025-01-15</td>\n",
       "      <td>506</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Start         End  Duration Day of Week\n",
       "0  2025-04-04  2025-09-16       165      Friday\n",
       "1  2024-01-30  2024-10-17       261     Tuesday\n",
       "2  2023-08-28  2025-01-15       506      Monday"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let us verify\n",
    "df[[\"Start\", \"End\", \"Duration\", \"Day of Week\"]].head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4 - Final Cleanup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we can also get rid of the cmp_name, and user and address columns,\n",
    "final_columns = [\n",
    "    \"Type\",\n",
    "    \"Start\",\n",
    "    \"End\",\n",
    "    \"Duration\",\n",
    "    \"Day of Week\",\n",
    "    \"Budget\",\n",
    "    \"Currency\",\n",
    "    \"Clicks\",\n",
    "    \"Impressions\",\n",
    "    \"Spent\",\n",
    "    \"CTR\",\n",
    "    \"CPC\",\n",
    "    \"CPI\",\n",
    "    \"Target Age\",\n",
    "    \"Target Gender\",\n",
    "    \"Username\",\n",
    "    \"Email\",\n",
    "    \"Name\",\n",
    "    \"Gender\",\n",
    "    \"Age\",\n",
    "]\n",
    "df = df[final_columns]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5 - Saving to a file in different formats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CSV format\n",
    "df.to_csv(\"df.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# JSON format\n",
    "df.to_json(\"df.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Spreadsheet format\n",
    "df.to_excel(\"df.xlsx\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6 - Visualizing results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First let's take care of the graphics, we configure the `matplotlib` plot style and set the font family to `serif`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib widget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Make the graphs nicer\n",
    "plt.style.use([\"classic\", \"ggplot\"])\n",
    "\n",
    "# Use a serif font\n",
    "plt.rc(\"font\", family=\"serif\")\n",
    "\n",
    "# Save high resolution figures suitable for printing\n",
    "plt.rc(\"savefig\", dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Duration</th>\n",
       "      <th>Budget</th>\n",
       "      <th>Clicks</th>\n",
       "      <th>Impressions</th>\n",
       "      <th>Spent</th>\n",
       "      <th>CTR</th>\n",
       "      <th>CPC</th>\n",
       "      <th>CPI</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5065.000000</td>\n",
       "      <td>5065.000000</td>\n",
       "      <td>5065.000000</td>\n",
       "      <td>5065.000000</td>\n",
       "      <td>5065.000000</td>\n",
       "      <td>5065.000000</td>\n",
       "      <td>5065.000000</td>\n",
       "      <td>5065.000000</td>\n",
       "      <td>5065.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>369.488253</td>\n",
       "      <td>502965.054097</td>\n",
       "      <td>40265.781639</td>\n",
       "      <td>499999.474630</td>\n",
       "      <td>253389.854689</td>\n",
       "      <td>0.080532</td>\n",
       "      <td>10.131895</td>\n",
       "      <td>0.506780</td>\n",
       "      <td>53.882330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>209.852969</td>\n",
       "      <td>290468.998656</td>\n",
       "      <td>21840.783154</td>\n",
       "      <td>2.023801</td>\n",
       "      <td>222774.897138</td>\n",
       "      <td>0.043682</td>\n",
       "      <td>19.527218</td>\n",
       "      <td>0.445550</td>\n",
       "      <td>21.170077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1764.000000</td>\n",
       "      <td>899.000000</td>\n",
       "      <td>499992.000000</td>\n",
       "      <td>107.000000</td>\n",
       "      <td>0.001798</td>\n",
       "      <td>0.001493</td>\n",
       "      <td>0.000214</td>\n",
       "      <td>18.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>192.000000</td>\n",
       "      <td>251171.000000</td>\n",
       "      <td>22575.000000</td>\n",
       "      <td>499998.000000</td>\n",
       "      <td>67071.000000</td>\n",
       "      <td>0.045150</td>\n",
       "      <td>1.756140</td>\n",
       "      <td>0.134143</td>\n",
       "      <td>35.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>371.000000</td>\n",
       "      <td>500694.000000</td>\n",
       "      <td>36746.000000</td>\n",
       "      <td>499999.000000</td>\n",
       "      <td>187743.000000</td>\n",
       "      <td>0.073493</td>\n",
       "      <td>5.207316</td>\n",
       "      <td>0.375486</td>\n",
       "      <td>54.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>554.000000</td>\n",
       "      <td>756850.000000</td>\n",
       "      <td>55817.000000</td>\n",
       "      <td>500001.000000</td>\n",
       "      <td>391790.000000</td>\n",
       "      <td>0.111634</td>\n",
       "      <td>11.630131</td>\n",
       "      <td>0.783572</td>\n",
       "      <td>72.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>730.000000</td>\n",
       "      <td>999565.000000</td>\n",
       "      <td>98379.000000</td>\n",
       "      <td>500007.000000</td>\n",
       "      <td>984705.000000</td>\n",
       "      <td>0.196758</td>\n",
       "      <td>462.814233</td>\n",
       "      <td>1.969410</td>\n",
       "      <td>90.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Duration         Budget        Clicks    Impressions          Spent  \\\n",
       "count  5065.000000    5065.000000   5065.000000    5065.000000    5065.000000   \n",
       "mean    369.488253  502965.054097  40265.781639  499999.474630  253389.854689   \n",
       "std     209.852969  290468.998656  21840.783154       2.023801  222774.897138   \n",
       "min       1.000000    1764.000000    899.000000  499992.000000     107.000000   \n",
       "25%     192.000000  251171.000000  22575.000000  499998.000000   67071.000000   \n",
       "50%     371.000000  500694.000000  36746.000000  499999.000000  187743.000000   \n",
       "75%     554.000000  756850.000000  55817.000000  500001.000000  391790.000000   \n",
       "max     730.000000  999565.000000  98379.000000  500007.000000  984705.000000   \n",
       "\n",
       "               CTR          CPC          CPI          Age  \n",
       "count  5065.000000  5065.000000  5065.000000  5065.000000  \n",
       "mean      0.080532    10.131895     0.506780    53.882330  \n",
       "std       0.043682    19.527218     0.445550    21.170077  \n",
       "min       0.001798     0.001493     0.000214    18.000000  \n",
       "25%       0.045150     1.756140     0.134143    35.000000  \n",
       "50%       0.073493     5.207316     0.375486    54.000000  \n",
       "75%       0.111634    11.630131     0.783572    72.000000  \n",
       "max       0.196758   462.814233     1.969410    90.000000  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "68924bdd471d4b84b00a7d9548dabd1f",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img src='' width=1280.0/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[[\"Budget\", \"Spent\", \"Clicks\", \"Impressions\"]].hist(\n",
    "    bins=16, figsize=(16, 6)\n",
    ")\n",
    "plt.savefig(\"Figure13.4.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "84483d4f9a12419aa7b84e1d197f2ec3",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img src='' width=1280.0/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[[\"CTR\", \"CPC\", \"CPI\"]].hist(bins=20, figsize=(16, 6))\n",
    "plt.savefig(\"Figure13.5.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2c7ecb29547349e69a03e6df8bb42f47",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img src='' width=1280.0/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's see the campaigns whose spent is > than 75% of the budget\n",
    "selector = df.Spent > df.Budget * 0.75\n",
    "df[selector][[\"Budget\", \"Spent\", \"Clicks\", \"Impressions\"]].hist(\n",
    "    bins=15, figsize=(16, 6), color=\"green\"\n",
    ")\n",
    "plt.savefig(\"Figure13.6.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0b4dfe5f304f4001909f1be1c9f2e250",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img src='' width=1280.0/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's aggregate by Day of the Week\n",
    "df_weekday = df.groupby([\"Day of Week\"]).sum(numeric_only=True)\n",
    "df_weekday[[\"Impressions\", \"Spent\", \"Clicks\"]].plot(\n",
    "    figsize=(16, 6), subplots=True\n",
    ")\n",
    "plt.savefig(\"Figure13.7.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">Impressions</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Spent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Target Gender</th>\n",
       "      <th>Target Age</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">A</th>\n",
       "      <th>20-25</th>\n",
       "      <td>499999.245614</td>\n",
       "      <td>2.189918</td>\n",
       "      <td>217330.771930</td>\n",
       "      <td>204518.652595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20-30</th>\n",
       "      <td>499999.465517</td>\n",
       "      <td>2.210148</td>\n",
       "      <td>252261.637931</td>\n",
       "      <td>228932.088945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20-35</th>\n",
       "      <td>499998.564103</td>\n",
       "      <td>1.774006</td>\n",
       "      <td>218726.410256</td>\n",
       "      <td>215060.976707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20-40</th>\n",
       "      <td>499999.459016</td>\n",
       "      <td>1.971241</td>\n",
       "      <td>255598.213115</td>\n",
       "      <td>222697.755231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20-45</th>\n",
       "      <td>499999.574074</td>\n",
       "      <td>2.245346</td>\n",
       "      <td>216527.666667</td>\n",
       "      <td>190345.252888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">M</th>\n",
       "      <th>45-50</th>\n",
       "      <td>499999.480769</td>\n",
       "      <td>2.128153</td>\n",
       "      <td>276112.557692</td>\n",
       "      <td>226975.008137</td>\n",
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       "    <tr>\n",
       "      <th>45-55</th>\n",
       "      <td>499999.306122</td>\n",
       "      <td>2.053494</td>\n",
       "      <td>267137.938776</td>\n",
       "      <td>239249.474145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45-60</th>\n",
       "      <td>499999.500000</td>\n",
       "      <td>1.984063</td>\n",
       "      <td>236623.312500</td>\n",
       "      <td>223464.578371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45-65</th>\n",
       "      <td>499999.679245</td>\n",
       "      <td>1.503503</td>\n",
       "      <td>215634.528302</td>\n",
       "      <td>223308.046968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45-70</th>\n",
       "      <td>499998.870370</td>\n",
       "      <td>1.822773</td>\n",
       "      <td>310267.944444</td>\n",
       "      <td>242353.980346</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>90 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Impressions                    Spent  \\\n",
       "                                   mean       std           mean   \n",
       "Target Gender Target Age                                           \n",
       "A             20-25       499999.245614  2.189918  217330.771930   \n",
       "              20-30       499999.465517  2.210148  252261.637931   \n",
       "              20-35       499998.564103  1.774006  218726.410256   \n",
       "              20-40       499999.459016  1.971241  255598.213115   \n",
       "              20-45       499999.574074  2.245346  216527.666667   \n",
       "...                                 ...       ...            ...   \n",
       "M             45-50       499999.480769  2.128153  276112.557692   \n",
       "              45-55       499999.306122  2.053494  267137.938776   \n",
       "              45-60       499999.500000  1.984063  236623.312500   \n",
       "              45-65       499999.679245  1.503503  215634.528302   \n",
       "              45-70       499998.870370  1.822773  310267.944444   \n",
       "\n",
       "                                         \n",
       "                                    std  \n",
       "Target Gender Target Age                 \n",
       "A             20-25       204518.652595  \n",
       "              20-30       228932.088945  \n",
       "              20-35       215060.976707  \n",
       "              20-40       222697.755231  \n",
       "              20-45       190345.252888  \n",
       "...                                 ...  \n",
       "M             45-50       226975.008137  \n",
       "              45-55       239249.474145  \n",
       "              45-60       223464.578371  \n",
       "              45-65       223308.046968  \n",
       "              45-70       242353.980346  \n",
       "\n",
       "[90 rows x 4 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's aggregate by gender and age\n",
    "agg_config = {\n",
    "    \"Impressions\": [\"mean\", \"std\"],\n",
    "    \"Spent\": [\"mean\", \"std\"],\n",
    "}\n",
    "\n",
    "df.groupby([\"Target Gender\", \"Target Age\"]).agg(agg_config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">Clicks</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Impressions</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Spent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Target Gender</th>\n",
       "      <th>A</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "      <th>A</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "      <th>A</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
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       "    <tr>\n",
       "      <th>Target Age</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20-25</th>\n",
       "      <td>2460345</td>\n",
       "      <td>2355790</td>\n",
       "      <td>2954169</td>\n",
       "      <td>28499957</td>\n",
       "      <td>29999964</td>\n",
       "      <td>34999963</td>\n",
       "      <td>12387854</td>\n",
       "      <td>16271204</td>\n",
       "      <td>14605751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20-30</th>\n",
       "      <td>2254458</td>\n",
       "      <td>1742729</td>\n",
       "      <td>2133740</td>\n",
       "      <td>28999969</td>\n",
       "      <td>21999963</td>\n",
       "      <td>25999959</td>\n",
       "      <td>14631175</td>\n",
       "      <td>11435369</td>\n",
       "      <td>13184984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20-35</th>\n",
       "      <td>1323341</td>\n",
       "      <td>1735301</td>\n",
       "      <td>2926626</td>\n",
       "      <td>19499944</td>\n",
       "      <td>22499974</td>\n",
       "      <td>34999942</td>\n",
       "      <td>8530330</td>\n",
       "      <td>10987452</td>\n",
       "      <td>18383305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20-40</th>\n",
       "      <td>2304325</td>\n",
       "      <td>1987013</td>\n",
       "      <td>1975200</td>\n",
       "      <td>30499967</td>\n",
       "      <td>28499973</td>\n",
       "      <td>26999950</td>\n",
       "      <td>15591491</td>\n",
       "      <td>15490069</td>\n",
       "      <td>13806347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20-45</th>\n",
       "      <td>2402785</td>\n",
       "      <td>1667405</td>\n",
       "      <td>1790037</td>\n",
       "      <td>26999977</td>\n",
       "      <td>22499993</td>\n",
       "      <td>21999968</td>\n",
       "      <td>11692494</td>\n",
       "      <td>9064229</td>\n",
       "      <td>9623006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25-30</th>\n",
       "      <td>2554594</td>\n",
       "      <td>2112054</td>\n",
       "      <td>1654070</td>\n",
       "      <td>28999991</td>\n",
       "      <td>24499954</td>\n",
       "      <td>21999959</td>\n",
       "      <td>21384048</td>\n",
       "      <td>10951922</td>\n",
       "      <td>11336787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25-35</th>\n",
       "      <td>2262695</td>\n",
       "      <td>2688949</td>\n",
       "      <td>1552947</td>\n",
       "      <td>27499945</td>\n",
       "      <td>32499993</td>\n",
       "      <td>17999977</td>\n",
       "      <td>15835588</td>\n",
       "      <td>17568960</td>\n",
       "      <td>10440487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25-40</th>\n",
       "      <td>2312824</td>\n",
       "      <td>2765216</td>\n",
       "      <td>1849825</td>\n",
       "      <td>25999955</td>\n",
       "      <td>30999968</td>\n",
       "      <td>27499964</td>\n",
       "      <td>12218317</td>\n",
       "      <td>12534598</td>\n",
       "      <td>15152483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25-45</th>\n",
       "      <td>2636550</td>\n",
       "      <td>2302052</td>\n",
       "      <td>2359336</td>\n",
       "      <td>28499946</td>\n",
       "      <td>28999987</td>\n",
       "      <td>28999965</td>\n",
       "      <td>14611597</td>\n",
       "      <td>13040278</td>\n",
       "      <td>14584821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25-50</th>\n",
       "      <td>2218490</td>\n",
       "      <td>2068886</td>\n",
       "      <td>2127728</td>\n",
       "      <td>29499980</td>\n",
       "      <td>25499953</td>\n",
       "      <td>26999972</td>\n",
       "      <td>14070093</td>\n",
       "      <td>10688162</td>\n",
       "      <td>15336580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30-35</th>\n",
       "      <td>2468621</td>\n",
       "      <td>1910432</td>\n",
       "      <td>3099221</td>\n",
       "      <td>34999963</td>\n",
       "      <td>25499993</td>\n",
       "      <td>33499969</td>\n",
       "      <td>23357728</td>\n",
       "      <td>11656842</td>\n",
       "      <td>18382424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30-40</th>\n",
       "      <td>2298939</td>\n",
       "      <td>2589451</td>\n",
       "      <td>2292862</td>\n",
       "      <td>28999966</td>\n",
       "      <td>31499972</td>\n",
       "      <td>26999968</td>\n",
       "      <td>15448034</td>\n",
       "      <td>13799337</td>\n",
       "      <td>13037479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30-45</th>\n",
       "      <td>2616444</td>\n",
       "      <td>2048272</td>\n",
       "      <td>2254996</td>\n",
       "      <td>31499930</td>\n",
       "      <td>24000017</td>\n",
       "      <td>26999971</td>\n",
       "      <td>14213835</td>\n",
       "      <td>9756196</td>\n",
       "      <td>12721961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30-50</th>\n",
       "      <td>2676730</td>\n",
       "      <td>2992745</td>\n",
       "      <td>1691940</td>\n",
       "      <td>35499952</td>\n",
       "      <td>34999971</td>\n",
       "      <td>22000001</td>\n",
       "      <td>22490468</td>\n",
       "      <td>19251726</td>\n",
       "      <td>10650649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30-55</th>\n",
       "      <td>2285163</td>\n",
       "      <td>2042317</td>\n",
       "      <td>1896148</td>\n",
       "      <td>27499990</td>\n",
       "      <td>25999977</td>\n",
       "      <td>23999993</td>\n",
       "      <td>14167102</td>\n",
       "      <td>13839757</td>\n",
       "      <td>10990401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35-40</th>\n",
       "      <td>2221694</td>\n",
       "      <td>2052499</td>\n",
       "      <td>2371541</td>\n",
       "      <td>27499963</td>\n",
       "      <td>24499985</td>\n",
       "      <td>28999970</td>\n",
       "      <td>14353641</td>\n",
       "      <td>9535254</td>\n",
       "      <td>13945054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35-45</th>\n",
       "      <td>1669214</td>\n",
       "      <td>2444734</td>\n",
       "      <td>2336965</td>\n",
       "      <td>23499973</td>\n",
       "      <td>31499915</td>\n",
       "      <td>26499968</td>\n",
       "      <td>11028491</td>\n",
       "      <td>16333287</td>\n",
       "      <td>12104330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35-50</th>\n",
       "      <td>2079197</td>\n",
       "      <td>2210849</td>\n",
       "      <td>2636434</td>\n",
       "      <td>28999969</td>\n",
       "      <td>32499959</td>\n",
       "      <td>29499985</td>\n",
       "      <td>17444423</td>\n",
       "      <td>15951931</td>\n",
       "      <td>18302824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35-55</th>\n",
       "      <td>2032124</td>\n",
       "      <td>2696916</td>\n",
       "      <td>2050578</td>\n",
       "      <td>24999971</td>\n",
       "      <td>31999968</td>\n",
       "      <td>27999975</td>\n",
       "      <td>15266919</td>\n",
       "      <td>21385299</td>\n",
       "      <td>13371221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35-60</th>\n",
       "      <td>2568010</td>\n",
       "      <td>2490988</td>\n",
       "      <td>2288088</td>\n",
       "      <td>26499980</td>\n",
       "      <td>31499959</td>\n",
       "      <td>28499984</td>\n",
       "      <td>14889775</td>\n",
       "      <td>15658231</td>\n",
       "      <td>13413552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40-45</th>\n",
       "      <td>2273514</td>\n",
       "      <td>2551116</td>\n",
       "      <td>2530067</td>\n",
       "      <td>28499979</td>\n",
       "      <td>31999953</td>\n",
       "      <td>29999956</td>\n",
       "      <td>12504560</td>\n",
       "      <td>16291722</td>\n",
       "      <td>14740888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40-50</th>\n",
       "      <td>2657318</td>\n",
       "      <td>2174374</td>\n",
       "      <td>2724277</td>\n",
       "      <td>31999966</td>\n",
       "      <td>25499999</td>\n",
       "      <td>30999968</td>\n",
       "      <td>15890075</td>\n",
       "      <td>13345592</td>\n",
       "      <td>15082723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40-55</th>\n",
       "      <td>2496766</td>\n",
       "      <td>2017712</td>\n",
       "      <td>1996308</td>\n",
       "      <td>32499956</td>\n",
       "      <td>23500003</td>\n",
       "      <td>28500012</td>\n",
       "      <td>16731630</td>\n",
       "      <td>13797387</td>\n",
       "      <td>12767689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40-60</th>\n",
       "      <td>2258586</td>\n",
       "      <td>1994181</td>\n",
       "      <td>1977123</td>\n",
       "      <td>30499976</td>\n",
       "      <td>24499978</td>\n",
       "      <td>27499981</td>\n",
       "      <td>16366979</td>\n",
       "      <td>12751785</td>\n",
       "      <td>12815735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40-65</th>\n",
       "      <td>2353368</td>\n",
       "      <td>2474574</td>\n",
       "      <td>2531935</td>\n",
       "      <td>28999981</td>\n",
       "      <td>27999981</td>\n",
       "      <td>31999969</td>\n",
       "      <td>14228005</td>\n",
       "      <td>14065243</td>\n",
       "      <td>17911485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45-50</th>\n",
       "      <td>2363777</td>\n",
       "      <td>2752142</td>\n",
       "      <td>1898628</td>\n",
       "      <td>29999959</td>\n",
       "      <td>33499945</td>\n",
       "      <td>25999973</td>\n",
       "      <td>13052723</td>\n",
       "      <td>17520280</td>\n",
       "      <td>14357853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45-55</th>\n",
       "      <td>2898843</td>\n",
       "      <td>2411690</td>\n",
       "      <td>1929080</td>\n",
       "      <td>30499970</td>\n",
       "      <td>30500003</td>\n",
       "      <td>24499966</td>\n",
       "      <td>13487602</td>\n",
       "      <td>15002737</td>\n",
       "      <td>13089759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45-60</th>\n",
       "      <td>2323099</td>\n",
       "      <td>2628039</td>\n",
       "      <td>2698876</td>\n",
       "      <td>29499980</td>\n",
       "      <td>31999972</td>\n",
       "      <td>31999968</td>\n",
       "      <td>16797214</td>\n",
       "      <td>13671906</td>\n",
       "      <td>15143892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45-65</th>\n",
       "      <td>2451874</td>\n",
       "      <td>1892687</td>\n",
       "      <td>1983735</td>\n",
       "      <td>30499968</td>\n",
       "      <td>25499983</td>\n",
       "      <td>26499983</td>\n",
       "      <td>15119530</td>\n",
       "      <td>11422840</td>\n",
       "      <td>11428630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45-70</th>\n",
       "      <td>2060369</td>\n",
       "      <td>1841268</td>\n",
       "      <td>2007266</td>\n",
       "      <td>26499968</td>\n",
       "      <td>22499975</td>\n",
       "      <td>26999939</td>\n",
       "      <td>13907778</td>\n",
       "      <td>11182951</td>\n",
       "      <td>16754469</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Clicks                   Impressions                      \\\n",
       "Target Gender        A        F        M           A         F         M   \n",
       "Target Age                                                                 \n",
       "20-25          2460345  2355790  2954169    28499957  29999964  34999963   \n",
       "20-30          2254458  1742729  2133740    28999969  21999963  25999959   \n",
       "20-35          1323341  1735301  2926626    19499944  22499974  34999942   \n",
       "20-40          2304325  1987013  1975200    30499967  28499973  26999950   \n",
       "20-45          2402785  1667405  1790037    26999977  22499993  21999968   \n",
       "25-30          2554594  2112054  1654070    28999991  24499954  21999959   \n",
       "25-35          2262695  2688949  1552947    27499945  32499993  17999977   \n",
       "25-40          2312824  2765216  1849825    25999955  30999968  27499964   \n",
       "25-45          2636550  2302052  2359336    28499946  28999987  28999965   \n",
       "25-50          2218490  2068886  2127728    29499980  25499953  26999972   \n",
       "30-35          2468621  1910432  3099221    34999963  25499993  33499969   \n",
       "30-40          2298939  2589451  2292862    28999966  31499972  26999968   \n",
       "30-45          2616444  2048272  2254996    31499930  24000017  26999971   \n",
       "30-50          2676730  2992745  1691940    35499952  34999971  22000001   \n",
       "30-55          2285163  2042317  1896148    27499990  25999977  23999993   \n",
       "35-40          2221694  2052499  2371541    27499963  24499985  28999970   \n",
       "35-45          1669214  2444734  2336965    23499973  31499915  26499968   \n",
       "35-50          2079197  2210849  2636434    28999969  32499959  29499985   \n",
       "35-55          2032124  2696916  2050578    24999971  31999968  27999975   \n",
       "35-60          2568010  2490988  2288088    26499980  31499959  28499984   \n",
       "40-45          2273514  2551116  2530067    28499979  31999953  29999956   \n",
       "40-50          2657318  2174374  2724277    31999966  25499999  30999968   \n",
       "40-55          2496766  2017712  1996308    32499956  23500003  28500012   \n",
       "40-60          2258586  1994181  1977123    30499976  24499978  27499981   \n",
       "40-65          2353368  2474574  2531935    28999981  27999981  31999969   \n",
       "45-50          2363777  2752142  1898628    29999959  33499945  25999973   \n",
       "45-55          2898843  2411690  1929080    30499970  30500003  24499966   \n",
       "45-60          2323099  2628039  2698876    29499980  31999972  31999968   \n",
       "45-65          2451874  1892687  1983735    30499968  25499983  26499983   \n",
       "45-70          2060369  1841268  2007266    26499968  22499975  26999939   \n",
       "\n",
       "                  Spent                      \n",
       "Target Gender         A         F         M  \n",
       "Target Age                                   \n",
       "20-25          12387854  16271204  14605751  \n",
       "20-30          14631175  11435369  13184984  \n",
       "20-35           8530330  10987452  18383305  \n",
       "20-40          15591491  15490069  13806347  \n",
       "20-45          11692494   9064229   9623006  \n",
       "25-30          21384048  10951922  11336787  \n",
       "25-35          15835588  17568960  10440487  \n",
       "25-40          12218317  12534598  15152483  \n",
       "25-45          14611597  13040278  14584821  \n",
       "25-50          14070093  10688162  15336580  \n",
       "30-35          23357728  11656842  18382424  \n",
       "30-40          15448034  13799337  13037479  \n",
       "30-45          14213835   9756196  12721961  \n",
       "30-50          22490468  19251726  10650649  \n",
       "30-55          14167102  13839757  10990401  \n",
       "35-40          14353641   9535254  13945054  \n",
       "35-45          11028491  16333287  12104330  \n",
       "35-50          17444423  15951931  18302824  \n",
       "35-55          15266919  21385299  13371221  \n",
       "35-60          14889775  15658231  13413552  \n",
       "40-45          12504560  16291722  14740888  \n",
       "40-50          15890075  13345592  15082723  \n",
       "40-55          16731630  13797387  12767689  \n",
       "40-60          16366979  12751785  12815735  \n",
       "40-65          14228005  14065243  17911485  \n",
       "45-50          13052723  17520280  14357853  \n",
       "45-55          13487602  15002737  13089759  \n",
       "45-60          16797214  13671906  15143892  \n",
       "45-65          15119530  11422840  11428630  \n",
       "45-70          13907778  11182951  16754469  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# finally, let's make a pivot table\n",
    "df.pivot_table(\n",
    "    values=[\"Impressions\", \"Clicks\", \"Spent\"],\n",
    "    index=[\"Target Age\"],\n",
    "    columns=[\"Target Gender\"],\n",
    "    aggfunc=\"sum\",\n",
    ")"
   ]
  }
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